title: PET-based treatment response evaluation in rectal cancer: prediction and validation.
abstract: OBJECTIVE
  To develop a positron emission tomography (PET)-based response prediction model to differentiate pathological responders from nonresponders. The predictive strength of the model was validated in a second patient group, treated and imaged identical to the patients on which the predictive model was based.
  
  
  METHODS
  Fifty-one rectal cancer patients were prospectively included in this study. All patients underwent fluorodeoxyglucose (FDG) PET-computed tomography (CT) imaging both before the start of chemoradiotherapy (CRT) and after 2 weeks of treatment. Preoperative treatment with CRT was followed by a total mesorectal excision. From the resected specimen, the tumor regression grade (TRG) was scored according to the Mandard criteria. From one patient group (n = 30), the metabolic treatment response was correlated with the pathological treatment response, resulting in a receiver operating characteristic (ROC) curve based cutoff value for the reduction of maximum standardized uptake value (SUV(max)) within the tumor to differentiate pathological responders (TRG 1-2) from nonresponders (TRG 3-5). The applicability of the selected cutoff value for new patients was validated in a second patient group (n = 21).
  
  
  RESULTS
  When correlating the metabolic and pathological treatment response for the first patient group using ROC curve analysis (area under the curve = 0.98), a cutoff value of 48% SUV(max) reduction was selected to differentiate pathological responders from nonresponders (specificity of 100%, sensitivity of 64%). Applying this cutoff value to the second patient group resulted in a specificity and sensitivity of, respectively, 93% and 83%, with only one of the pathological nonresponders being false positively predicted as pathological responding.
  
  
  CONCLUSIONS
  For rectal cancer, an accurate PET-based prediction of the pathological treatment response is feasible already after 2 weeks of CRT. The presented predictive model could be used to select patients to be considered for less invasive surgical interventions or even a "wait and see" policy. Also, based on the predicted response, early modifications of the treatment protocol are possible, which might result in an improved clinical outcome.
authors.affiliation: Department of Radiation Oncology, MAASTRO, GROW Research Institute, University Medical Centre Maastricht, Maastricht, the Netherlands. marco.janssen@maastro.nl
authors.forename: Marco H M
authors.initials: MH
authors.lastname: Janssen
authors.forename: Michel C
authors.initials: MC
authors.lastname: Öllers
authors.forename: Ruud G P M
authors.initials: RG
authors.lastname: van Stiphout
authors.forename: Robert G
authors.initials: RG
authors.lastname: Riedl
authors.forename: Jørgen
authors.initials: J
authors.lastname: van den Bogaard
authors.forename: Jeroen
authors.initials: J
authors.lastname: Buijsen
authors.forename: Philippe
authors.initials: P
authors.lastname: Lambin
authors.forename: Guido
authors.initials: G
authors.lastname: Lammering
mesh_terms: D059248:Chemoradiotherapy / Q000379:methods*; D019788:Fluorodeoxyglucose F18 / Q000493:pharmacokinetics; D006801:Humans; D064847:Multimodal Imaging; D009367:Neoplasm Staging; D049268:Positron-Emission Tomography / Q000379:methods*; D011446:Prospective Studies; D012372:ROC Curve; D019275:Radiopharmaceuticals / Q000493:pharmacokinetics; D012004:Rectal Neoplasms / Q000000981:diagnostic imaging* / Q000378:metabolism / Q000473:pathology / Q000628:therapy*; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed; D047368:Tumor Burden
chemical_list: D019275:Radiopharmaceuticals; D019788:Fluorodeoxyglucose F18